Robustness of the filtered-x LMS algorithm: part 11: robustness enhancement by minimal regularization for norm bounded uncertainty


Fraanje, R., Elliott, S.J. and Verhaegen, M. (2007) Robustness of the filtered-x LMS algorithm: part 11: robustness enhancement by minimal regularization for norm bounded uncertainty. IEEE Transactions on Signal Processing, 55, (8), 4038-4047. (doi:10.1109/TSP.2007.896086).

Download

Full text not available from this repository.

Original Publication URL: http://dx.doi.org/10.1109/TSP.2007.896086

Description/Abstract

The relationship between the regularization methods proposed in the literature to increase the robustness of the filtered-X LMS (FXLMS) algorithm is discussed. It is shown that the existing methods are special cases of a more general robust FXLMS algorithm in which particular filters determine the type of regularization. Based on the analysis by Fraanje, Verhaegen, and Elliott [ldquorobustness of the filtered-X LMS algorithm - part I: necessary conditions for convergence and the asymptotic pseudospectrum of Toeplitz Matricesrdquo of this issue], regularization filters are designed that guarantee that the strictly positive real conditions for asymptotic convergence or noncritical behavior are just satisfied for all uncertain systems contained in a particular norm bounded set.

Item Type: Article
Additional Information:
ISSNs: 1053-587X (print)
Related URLs:
Subjects: Q Science > Q Science (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: University Structure - Pre August 2011 > Institute of Sound and Vibration Research > Signal Processing and Control
ePrint ID: 49556
Date Deposited: 15 Nov 2007
Last Modified: 27 Mar 2014 18:33
URI: http://eprints.soton.ac.uk/id/eprint/49556

Actions (login required)

View Item View Item